Method for performing automated in-scene based atmospheric compensation for multi-and hyperspectral imaging sensors in the solar reflective spectral region

ABSTRACT

A method of automatically compensating a multi- or hyper-spectral, multi-pixel image for atmospheric effects, comprising resolving a plurality of spectrally-diverse pixels from the image, determining a spectral baseline from the spectrally-diverse pixels, determining a statistical spectral deviation of the spectrally-diverse pixels, normalizing the statistical spectral deviation by applying a scale factor, and compensating image pixels with both the spectral baseline and the normalized spectral deviation. Another embodiment features a method of automatically determining a measure of atmospheric aerosol optical properties using a multi- or hyper-spectral, multi-pixel image, comprising resolving a plurality of spectrally-diverse pixels from the image, determining a statistical spectral deviation of the spectrally-diverse pixels, correcting the statistical spectral deviation for non-aerosol transmittance losses, and deriving from the statistical spectral deviation one or more wavelength-dependent aerosol optical depths. A final embodiment features a method of automatically determining a measure of atmospheric gaseous optical properties using a multi- or hyper-spectral, multi-pixel image, comprising resolving a plurality of spectrally-diverse pixels from the image, determining a statistical spectral deviation of the spectrally-diverse pixels, and deriving from the statistical spectral deviation wavelength-dependent gaseous optical depths.

GOVERNMENT RIGHTS

[0001] This invention was made with Government support under ContractF19628-02-C-0054 awarded by the Department of the Air Force. TheGovernment has certain rights in this invention.

FIELD OF INVENTION

[0002] This invention relates to automated methods for correcting multi-and hyperspectral images of the earth's surfaces for atmospheric effectsand sensor calibration problems.

BACKGROUND OF THE INVENTION

[0003] The problem addressed here is the compensation of remotely sensedmulti- and hyperspectral images in the solar reflective spectral regime(λ<3000 nm) for the transmission losses and scattering effects of theintervening atmosphere. The problem is illustrated in FIG. 1 for a pixelcontaining vegetation as viewed from a space-based sensor. A number oflarge spectral depressions are seen which are primarily due toabsorption by gaseous water and to a lesser extent by carbon dioxide andoxygen. Below 700 nm, the observed reflectance exceeds the actualreflectance; this is due to atmospheric scattering by aerosols andmolecules. The apparent reflectance at the sensor is well represented by

ρ_(j)(λ)=A(λ)+B(λ)ρ_(j) ^(o)(λ)+C(λ)<ρ(λ)>,   (1)

[0004] where ρ_(j) is the observed reflectance (the radiance normalizedby the surface normal component of the solar flux) for the j'th pixel ata spectral band centered at wavelength λ, ρ_(j) ^(o) is the actualsurface reflectance, <ρ> is a spatially averaged surface reflectance,and A, B, and C are coefficients representing the transmission andscattering effects of the atmosphere. The first coefficient, A, accountsfor light which never encounters the surface and is scattered andabsorbed within the atmosphere. The second, B, accounts for thesun-surface-sensor path transmittance loss. The third, C, accounts forthe adjacency effect which is the cross talk between pixels induced byatmospheric scattering. The length scale of the adjacency effect istypically of order ˜0.5 km, thus <ρ> is a slowly varying function ofposition within a large image. It is noted that B and C also have a weakdependence on <ρ> through light that reflects off the surface and isscattered back to the surface by the atmosphere.

[0005] The aim of atmospheric compensation is to determine A, B, C and<ρ> by some means in order to invert Eq(1) to retrieve the actualsurface reflectance, ρ_(j) ^(o). The prior art is embodied in variousmethods described in the literature and summarized below.

[0006] The simplest and computationally fastest prior art methods foratmospheric correction are the “Empirical Line Method” (ELM) andvariants thereof, which may be found in the ENVI (Environment forVisualizing Images) software package of Research Systems, Inc. The ELMassumes that the radiance image contains some pixels of knownreflectance, and also that the radiance and reflectance values for eachwavelength channel of the sensor are linearly related, in theapproximation that A, B, C and <ρ> are constants of the image.Therefore, the image can be converted to reflectance by applying asimple gain and offset derived from the known pixels. This method ishowever not generally applicable, as in-scene known reflectances areoften not available. In variants of the ELM, approximate gain and offsetvalues are generated using pixels in the image that are treated as iftheir spectra were known. For example, in the Flat Field Method a singlebright pixel is taken as having a spectrally flat reflectance and theoffset is taken as zero; accordingly, dividing the image pixel spectraby the bright pixel spectrum yields approximate relative reflectances.In the Internal Average Relative Reflectance method this procedure isfollowed using a scene-average spectrum rather than a single brightpixel spectrum. In general, neither the Flat Field Method nor theAverage Relative Reflectance methods are very accurate.

[0007] More sophisticated prior art methods are based onfirst-principles computer modeling. These methods require extensive, andoften time-consuming, calculations with a radiative transfer code, suchas MODTRAN [Berk et al., 1998], in which A, B and C are computed for awide range of atmospheric conditions (aerosol and water column amountsand different surface reflectance values). The calculations may beperformed for each image to be analyzed, or may be performed ahead oftime and stored in large look-up tables. The appropriate parametervalues for the image are determined by fitting certain observed spectralfeatures, such as water vapor absorption bands, to the calculations. Forretrieving aerosol or haze properties such as the optical depth, methodsare available that rely on “dark” pixels, consisting of vegetation ordark soil [Kaufman et al., 1997] or water bodies. Commonly usedfirst-principles computer codes for atmospheric correction include:ATREM [Gao et al., 1996]; ACORN [R. Green, unpublished], available fromAnalytical Imaging and Geophysics LLC; FLAASH [Adler-Golden et al.,1999], developed by Spectral Sciences Inc. (SSI) and the Air ForceResearch Laboratory (AFRL); and ATCOR2 [Richter, 1996], used mainly formultispectral atmospheric correction.

SUMMARY OF THE INVENTION

[0008] The invention includes both a complete atmospheric correctionmethod, and methods for retrieving the wavelength-dependent opticaldepth of the aerosol or haze and molecular absorbers. The atmosphericcorrection method of the current invention, unlike first-principlesmethods, allows the retrieval of reasonably accurate reflectance spectraeven when the sensor does not have a proper radiometric or wavelengthcalibration, or when the solar illumination intensity is unknown, suchas when a cloud deck is present. The computational speed of theatmospheric correction method is superior to that of first-principlesmethods, making it potentially suitable for real-time applications. Theinvention determines the atmospheric compensation parameters directlyfrom the information contained within the scene (observed pixelspectra).

[0009] The aerosol optical depth retrieval method of the currentinvention, unlike prior art methods, does not require the presence ofdark pixels. The retrieved optical depth information can be utilized toimprove the accuracy of methods that use first-principles modeling. Inparticular, it can be used to set the optical depth of a model aerosolwhen dark pixels are unavailable, or to select from among alternativemodel aerosols to provide consistency between optical depths retrievedfrom a dark pixel method and from the current invention.

[0010] The underlying assumptions of the invention are:

[0011] 1. There are a number (≈10 or more) of diverse pixel spectra(diverse materials) in the scene, and

[0012] 2. The spectral standard deviation of ρ_(j) ^(o) for a collectionof diverse materials is a nearly wavelength-independent constant.

[0013] An additional, helpful assumption is:

[0014] 3. There are sufficiently dark pixels (ρ_(j) _(o)(λ)≈0) in ascene to allow for a good estimation of the nearly spatially invariantbaseline contribution, ρ_(b)=A+C<ρ>.

[0015] The first assumption is virtually always applicable, as it onlyrequires that a handful of pixels out of typically ˜10⁵ to 10⁶ pixelsdisplay diverse spectra. The most notable exception would be a sceneover completely open and deep water, in which case the materialreflectance is well known a priori. The diverse spectra can be selectedusing any of a number of spectral diversity metrics and algorithms. Thesecond assumption appears to be generally true based on empiricalobservation and is likely related to the lack of spectral correlationbetween diverse materials. The third assumption is frequentlyapplicable, as most scenes will contain a number of very dark pixelsfrom such surfaces as water bodies, vegetation, and cast shadows. Forthe atypical cases that violate this assumption, there are methods,described below, for estimating a reasonable baseline.

[0016] Under these assumptions, the spectral standard deviation of Eq(1) for a set of diverse pixel spectral can be expressed as,

σρ(λ)=B(λ)σρ^(o)(λ).   (2)

[0017] There is no contribution to the standard deviation from A or C<ρ>because they are the same for each pixel spectrum. Since σρ^(o) isassumed to be constant, then to within a normalization factor,designated g_(o), σρ represents one of the correction factors, B. Theactual surface spectral reflectance can be retrieved using the extractedin-scene determined compensation parameters via $\begin{matrix}{{\rho_{j}^{o}(\lambda)} = {\frac{{\rho_{j}(\lambda)} - {\rho_{b}(\lambda)}}{g_{o}\sigma \quad {\rho (\lambda)}}.}} & (3)\end{matrix}$

[0018] A key attribute of this invention is its applicability to anysensor viewing or solar elevation angle.

[0019] There are a number of methods to establish the normalizationfactor g_(o), which depends on sensor attributes. For many sensors thereis at least one atmospheric window band, typically in the 1500-2500 nmregion (see FIG. 1), for which B(λ)≈1 (inspection of FIG. 1 shows thatB=0.9 is a good estimate); thus for this band

g _(o)=0.9/σρ.   (4)

[0020] If a suitable window band is not available, the normalization canstill be extracted directly from the standard deviation curve. Two bands(λ₂>λ₁) are selected which are outside of any water absorption region,insuring that the atmospheric extinction is due primarily to theaerosols. The ratio of the standard deviations of these bands is adirect measure of the difference in aerosol optical depth τ via,$\begin{matrix}{{{- \ln}\quad \frac{\sigma \quad {\rho \left( \lambda_{1} \right)}}{\sigma \quad {\rho \left( \lambda_{2} \right)}}} = {{\tau \left( \lambda_{1} \right)} - {{\tau \left( \lambda_{2} \right)}.}}} & (5)\end{matrix}$

[0021] Depending on the wavelengths of the selected bands, a generallysmall correction for molecular Rayleigh scattering may be required.Standard and efficient methods are available for applying thiscorrection.

[0022] For aerosols, the ratio of optical depths at two wavelengths iswell approximated by the Angstrom formula, $\begin{matrix}{{\frac{\tau \quad \left( \lambda_{1} \right)}{\tau \quad \left( \lambda_{2} \right)} = \left( \frac{\lambda_{2}}{\lambda_{1}} \right)^{\alpha}},{\left( {\alpha > 0} \right).}} & (6)\end{matrix}$

[0023] For terrestrial aerosols α falls in the range 1>α>2, and we adoptα=1.5 for general estimation purposes. Combining Eqs. (5) and (6) allowsone to convert the optical depth difference to an absolute optical depthat either wavelength, $\begin{matrix}{{\tau \left( \lambda_{2} \right)} = {\frac{{- \ln}\quad \frac{\sigma \quad {\rho \left( \lambda_{1} \right)}}{\sigma \quad {\rho \left( \lambda_{2} \right)}}}{\left( \frac{\lambda_{2}}{\lambda_{1}} \right)^{\alpha} - 1}.}} & (7)\end{matrix}$

[0024] The normalization factor is now determined from

g _(o)=exp(−τ(λ₂))/σρ(λ₂).   (8)

[0025] It is noted that Eq(8) is just the generalization of Eq(4).

[0026] If the sensor radiometric calibration or the solar illuminationintensity are not known, then σρ is known only to within a scale factor,and the normalization factor go must be estimated by a different method.One method is to set g_(o) such that that the maximum retrievedreflectance value for any wavelength and pixel is unity. This method isfound to work reasonably in images containing a diversity of man-madematerials, such as urban scenes. Another method is to derive g_(o) bycomparing the retrieved reflectance values with those in a library ofmaterial spectra.

[0027] For most scenes, the baseline curve is defined as the darkestobserved signal for each band from among the diverse spectra. Thepresence of sufficiently dark pixels is indicated by at least one pixelspectrum with an apparent reflectance below ˜0.05 for λ>1500 nm. For therare situation that a dark spectrum is unavailable, it is still possibleto estimate a reasonable background. It is worth noting that this casearises because the pixel reflectances are generally much larger than thebaseline contribution, thus considerable uncertainty in the baselinevalues are tolerable. In this case, the baseline may be approximated asthe excess reflectance at the shorter wavelengths (where baselineeffects are most important) relative to a flat spectral reflectancematerial,

ρ_(b)(λ)=ρ_(b) ^(o)(λ)−βσρ(λ), (λ<1000 nm),   (9)

[0028] where ρ_(b) ^(o) is an initial baseline guess defined by thedarkest available channels, and β is adjusted such that ρ_(b)=0 at 1000nm (or some suitably nearby channel depending on the available sensorbands). The baseline is taken as zero for λ>1000 mn. An alternativemethod is to use a radiative-transfer code to compute the baseline basedon the retrieved aerosol and molecular optical properties. Other methodsfor estimating the baseline spectrum will be known to those skilled inthe art. These include a pairwise linear regression method [Crippen,1987] and a dark pixel method that incorporates a theoreticalrepresentation of the baseline's wavelength dependence [Chavez, 1988].

[0029] While the focus of the previous discussion was on atmosphericcompensation, it was noted that this invention provides, to within anormalization factor, the sun-surface-sensor path transmittance B(λ).Analysis of B can provide quantitative measures (column amounts) of allthe atmospheric attenuation sources, including aerosol scattering andabsorption and molecular absorption and Rayleigh scattering. This may beaccomplished through spectral fitting with an accurate atmosphericradiative-transfer code (e.g., MODTRAN), or alternatively through theuse of analytical approximations. Of most significance is that one canextract the detailed wavelength dependence of the aerosol extinctionwhich has not been accessible with previous multi- and hyperspectralimage analysis approaches.

[0030] It should be noted that the definition of a scene or image isflexible, in that it may include a sub-section of pixels from a largeroriginal data set. Thus, the current invention may be applied toindividual sub-sections of a scene or image, provided that a sufficientdiversity of pixel spectra exists within the sub-sections for computingan accurate standard deviation and baseline. In this way, spatialvariations in the adjacency averaged reflectance <ρ> and in theatmospheric parameters can be identified and taken into account in theatmospheric correction.

[0031] This invention features in one embodiment a method ofautomatically compensating a multi- or hyper-spectral, multi-pixel imagefor atmospheric effects, comprising resolving a plurality ofspectrally-diverse pixels from the image, determining a spectralbaseline from the spectrally-diverse pixels, determining a statisticalspectral deviation of the spectrally-diverse pixels, normalizing thestatistical spectral deviation by applying a scale factor, andcompensating image pixels with both the spectral baseline and thenormalized spectral deviation.

[0032] The compensating step may comprise subtracting the spectralbaseline from the image pixels to accomplish partially-compensatedpixels, and further, dividing the partially-compensated pixels by thenormalized spectral deviation. The resolving step may take place with aspectral end member selection algorithm or a clustering algorithm, andmay be accomplished manually. At least ten end members may be resolved.The resolving step may take place using a subset of spectral bands thatspan the spectrum of the image.

[0033] The method may further comprise screening anomalous pixels out ofthe image pixels before the resolving step. The screening step maycomprise removing pixels containing opaque clouds and cirrus clouds.

[0034] The spectral baseline determining step may take place using alinear regression method. The spectral baseline determining step maycomprise determining the excess reflectance at relatively shortwavelengths relative to a flat spectral reflectance material, or maycomprise determining the darkest signal from all of the image pixels foreach spectral band, or determining the darkest signal in any spectralband from all of the spectrally-diverse pixels, or subtracting aconstant reflectance contribution. The spectral baseline determiningstep in this case may further comprise setting the constant reflectancecontribution to match the image signal at a reference wavelength. Thespectral baseline determining step may use a radiative-transfer code tocompute the baseline based on the determined aerosol and molecularoptical properties

[0035] The statistical spectral deviation determining step may comprisedetermining the standard deviation of the spectrally-diverse pixels. Thenormalizing step may comprise normalizing in a spectral band in whichthe sun-surface-sensor path spectral transmittance factor is close tounity, or resolving at least two spectral window bands that do not havea significant contribution from water absorption, using the spectraldeviations of these window bands to retrieve an aerosol optical depth,and using the optical depth to normalize. The normalizing step mayinvolve establishing the scale factor such that the maximum retrievedreflectance value for any wavelength and pixel of the spectrally-diversepixels is unity. Alternatively, the normalizing step may comprisecomparing the spectrally-diverse pixels to a predetermined set ofspectra of different, known materials.

[0036] The image compensation method may further comprise refining thespectrally-diverse pixels to remove spectrally-diverse pixels thatcontain undesirable spectral features, the refining step taking placeafter the normalizing step and before the compensating step. Thisrefining step may comprise removing pixels with an abrupt reflectancechange around about 700 nm, or removing pixels that have the greatesteffect on the smoothness of the statistical spectral deviation, orintroducing a wavelength dependence into a normalization factor suchthat selected spectrally-diverse pixels are made to agree withcorresponding known library spectra as closely as possible.

[0037] This invention features in another embodiment a method ofautomatically determining a measure of atmospheric aerosol opticalproperties using a multi- or hyper-spectral, multi-pixel image,comprising resolving a plurality of spectrally-diverse pixels from theimage, determining a statistical spectral deviation of thespectrally-diverse pixels, correcting the statistical spectral deviationfor non-aerosol transmittance losses, and deriving from the statisticalspectral deviation one or more wavelength-dependent aerosol opticaldepths.

[0038] The statistical spectral deviation determining step may comprisedetermining the standard deviation of the spectrally-diverse pixels. Thecorrecting step may involve using a radiative transfer code. Thederiving step may also involve using a radiative transfer code. Thederiving step may alternatively comprises performing a least squares fitof the statistical spectral deviation to an analytical representation ofthe aerosol transmittance, or performing a least squares fit of thestatistical spectral deviation to a radiative transfer code.

[0039] In a final embodiment, this invention features a method ofautomatically determining a measure of atmospheric gaseous opticalproperties using a multi- or hyper-spectral, multi-pixel image,comprising resolving a plurality of spectrally-diverse pixels from theimage, determining a statistical spectral deviation of thespectrally-diverse pixels, and deriving from the statistical spectraldeviation wavelength-dependent gaseous optical depths.

[0040] The statistical spectral deviation determining step may comprisedetermining the standard deviation of the spectrally-diverse pixels. Thederiving step may comprise selecting spectral bands that are outside ofany water absorption region, and deriving a gaseous optical depth fromthe statistical spectral deviations at the selected bands.

BRIEF DESCRIPTION OF THE DRAWINGS

[0041]FIG. 1 is an example of observed and actual spectral reflectancecurves of a vegetation-containing pixel for a nadir-viewing space-basedsensor, useful in understanding the invention.

[0042]FIG. 2 is a data processing flow diagram for the preferredembodiment of the invention;

[0043]FIG. 3 shows selected spectral end members for a particularobservation;

[0044]FIG. 4 shows normalized and atmospherically compensated endmembers of FIG. 3;

[0045]FIG. 5 shows the effect of the end member refinement process ofthe preferred embodiment;

[0046]FIG. 6 is a comparison of atmospherically compensatedhyper-spectral data of the invention to ground truth measurements and tocompensated data based on the FLAASH code;

[0047]FIG. 7 is a comparison of atmospherically compensatedmulti-spectral data of the invention to compensated data based on theFLAASH code;

[0048]FIG. 8 is a data processing flow diagram for the preferredembodiment of the aerosol optical properties retrieval method of theinvention; and

[0049]FIG. 9 depicts examples of aerosol optical properties retrieval ofthe invention for both clear and hazy data.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0050]FIG. 2 depicts the data processing flow for the preferredembodiment. The sensor data 100 is comprised of multi- or hyperspectralimagery in which at least two spectral bands below 3000 nm areavailable. There is no upper limit to the number of spectral bands thatcan be handled. The input data can be in units of calibrated radiance orapparent spectral reflectance or even in uncalibrated raw counts. Thechoice of units only impacts the selection of normalization method 112.

[0051] A spectral end member selection algorithm 102 is used to select aplurality of spectrally-diverse pixels. While there are a number ofsuitable end member algorithms, the Spectral Sciences, Inc. SMACC(Sequential Maximum Angle Convex Cones) algorithm was utilized for itsexcellent computational efficiency. Other methods for selecting adiverse set of pixel spectra will be known to those skilled in the art,and may include clustering algorithms as well as end member algorithms;however, clustering algorithms are usually more computationallyintensive. The precise number of end members used for the compensationis not critical. 10 to 20 end members is typically sufficient. Animportant aspect of SMACC is that it finds end members in order ofdecreasing spectral diversity. This can afford a significantcomputational efficiency, since the end member selection process can beterminated after the pre-selected number of end members is attained. Forsensors containing more than ˜10 spectral bands it is computationallyefficient to limit the end member selection process to ˜10 bands. Use ofa subset of the total available number of bands does not impact thecompensation quality as long as the selected subset spans the sensorspectral coverage.

[0052]FIG. 3 displays end members selected from data taken by theairborne AVIRIS sensor (400-2500 nm, 224 bands, 512×512 scene pixels, 2m GSD (Ground Sampling Distance)). Note the diversity of the selectedspectra, a key aspect of this invention.

[0053] It is important to screen for and eliminate anomalous pixelspectra from the end member selection process. This includes pixelscontaining opaque clouds, thin cirrus clouds, and “bad” pixelscontaining sensor artifacts. Opaque clouds may be recognized using oneof two methods, depending on the available sensor bands. If bands areavailable in either of the 940 nm or 1140 nm water vapor absorptionbands, then opaque clouds can be recognized through anomalously smallabsorption depressions, as the clouds reside above most of the watervapor column. If the water bands are not available, then clouds can berecognized through a whiteness-brightness test; they are spectrally flat(white) and exhibit a high reflectance (bright). Thin cirrus is mosteasily flagged through an excess signal (cloud back scattering) in thevery dark 1380 nm water absorption band. Cirrus clouds occur at muchhigher altitudes than other clouds, and thus are detectable even in verystrongly absorbing water bands. Bad pixels are recognized through thepresence of anomalously high (saturated) or low (negative) spectralchannels. The screening thresholds for these types of anomalous pixelscan be set conservatively. Since a reasonably large number of endmembers are selected, it does not matter if a few legitimate spectra areeliminated in the screening process.

[0054] Spectral standard deviation and baseline determination 106 arethen performed on the selected end members. The methods for determiningthe baseline 110 and the conditions under which they are employed weredescribed above. Similarly, for calibrated data, the methods fordetermining the normalization factor g_(o) 112 were previouslydescribed. However, for uncalibrated data the normalization isdetermined and applied after the atmospheric compensation step 116. Inthis case, the brightest spectral channel from among all the compensatedend members is scaled to unit reflectance; the required scaling factoris g_(o).

[0055] Atmospheric compensation on the end members 116 is performedusing Eq. (3). The resulting compensated end members 118 for the AVIRISdata are presented in FIG. 4. At this point, an improvement in theconstancy of the standard deviation may be made by refining the endmember selection 120 to remove end members that contain undesirablespectral features, generally characterized by an abrupt change inreflectance. This most often occurs for vegetation, which has a sharpred edge around 700 nm. As indicated in FIG. 4, there are vegetation endmembers for the AVIRIS data. It is straightforward to automaticallyidentify and cull out vegetation spectra by searching for abruptreflectance changes between bands on either side of the red edge. Theimprovement in the standard deviation due to removal of vegetationspectra is apparent in FIG. 5.

[0056] Further refinements of the end member selection may also be madeby various methods. One method is to require that the end members beselected to agree with spectra contained in a library, or with linearcombinations of such library spectra, to within a certain threshold. Thelibrary spectra may also be used to select or refine the value of thenormalization factor g_(o) to obtain the best fit between the normalizedend members and the library spectra. In a generalization of the fittingstep, a wavelength dependence may be introduced into the normalizationfactor g_(o) such that the selected end members are made to agree withthe corresponding library spectra as closely as possible. Another methodfor end member selection refinement is to require that the end membersobey a requirement of spectral smoothness, such as by setting an upperlimit on adjacent-channel differences; this represents a generalizationof the vegetation exclusion method.

[0057] The refined end members undergo the same standard deviationprocessing 124 as comprised by steps 106, 108, and 112, resulting in therefined normalized standard deviation 126. Finally, atmosphericcompensation 128 is performed on the entire sensor data set to yield thedesired end product, the surface spectral reflectance data cube 130(compensated spectra for all the pixels). This entails subtracting thebaseline and dividing by the refined normalized standard deviation. Theentire process flow is automated. Aside from the sensor data, the onlyexternally required inputs are the solar elevation angle for each dataset and specification of the available bands (band centers and widths)for the sensor.

[0058] The quality of the atmospheric compensation for the presentationinvention can be assessed by comparison to results from one of thestate-of-the-art atmospheric compensation codes, FLAASH. This comparisonis provided in FIG. 6. FLAASH required ˜10 min of computational time toperform its analysis whereas the present invention required under 1 minon the same computer (1.8 GHz Pentium IV PC). This invention also workswell for multi-spectral satellite data such as from the Landsat7 ETM+sensor (6 bands in the 450-2500 nm region with a 30 m GSD), as shown inFIG. 7.

[0059] The preferred embodiment for the aerosol optical propertiesretrieval is presented in FIG. 8. The starting point for the aerosolproperties retrieval is the refined un-normalized standard deviation 200which derives from the standard deviation processing 124 (see FIG. 2) ofthe end members. The un-normalized standard deviation is first correctedfor sun-surface-sensor transmittance losses due to Rayleigh scattering.This may be accomplished either through the use of an accurateradiative-transfer code (e.g., MODTRAN) or through well-established andaccurate analytical approximations. While it is generally preferred toselect bands outside of the molecular absorption bands, this is not alsopossible for some sensors. In these cases, the molecular absorptioneffects 204 can be corrected through the use of an accurateradiative-transfer code in concert with specification of the molecularabsorber column amounts. The molecular column amounts may be obtainedeither by retrieval from the un-normalized spectral standard deviation200 itself if suitable bands are available using an atmosphericcompensation code such as FLAASH or by estimation based on a climatologydata base or measured weather conditions. The aerosol optical propertiesretrieval 206 is performed on the Rayleigh scattering and molecularabsorption compensated data. It proceeds in two steps. First, the bandsselected for the aerosol retrieval are ratioed to a reference band andthe resulting ratios are fit using the Angstrom formula in Eq. (6). Thisresults in the reference optical depth τ_(o) and wavelength scalingexponent α. Second, this also enables a more exact determination of thenormalization constant using Eq. (8), which can be employed in theatmospheric compensation processing. The use of the aerosol retrievalalgorithm is illustrated in FIG. 9 for examples of clear and hazy dataobtained by the AVIRIS sensor.

[0060] The molecular optical properties for each molecular absorptionfeature can also be retrieved from the un-normalized spectral standarddeviation 200. This requires at least three bands, a molecularabsorption band and two nearby, preferably flanking, reference bands (nomolecular absorption). By linear interpolation or extrapolation, thereference bands are used to estimate the zero-absorption signals foreach absorption band. The ratio of the absorption band signals to theircorresponding zero-absorption signals define the molecular transmittancefunction T(λ). The molecular optical depths can be retrieved fromτ(λ)=−lnT(λ). If the spectral absorption coefficients α(λ) are known forthe band, then the molecular column amount U can be retrieved from asingle wavelength by U=τ(λ)/α(λ).

[0061] Although specific features of the invention are shown in somedrawings and not others, this is for convenience only as some featuremay be combined with any or all of the other features in accordance withthe invention.

[0062] Other embodiments will occur to those skilled in the art and arewithin the following claims:

What is claimed is:
 1. A method of automatically compensating a multi-or hyper-spectral, multi-pixel image for atmospheric effects,comprising: resolving a plurality of spectrally-diverse pixels from theimage; determining a spectral baseline from the spectrally-diversepixels; determining a statistical spectral deviation of thespectrally-diverse pixels; normalizing the statistical spectraldeviation by applying a scale factor; and compensating image pixels withboth the spectral baseline and the normalized spectral deviation.
 2. Theimage compensation method of claim 1 wherein the compensating stepcomprises subtracting the spectral baseline from the image pixels toaccomplish partially-compensated pixels.
 3. The image compensationmethod of claim 2 wherein the compensating step further comprisesdividing the partially-compensated pixels by the normalized spectraldeviation.
 4. The image compensation method of claim 1 wherein theresolving step takes place with a spectral end member selectionalgorithm.
 5. The image compensation method of claim 1 wherein theresolving step takes place with a clustering algorithm.
 6. The imagecompensation method of claim 1 wherein the resolving step isaccomplished manually.
 7. The image compensation method of claim 1wherein at least ten end members are resolved.
 8. The image compensationmethod of claim 1 wherein the resolving step takes place using a subsetof spectral bands that span the spectrum of the image.
 9. The imagecompensation method of claim 1 further comprising screening anomalouspixels out of the image pixels before the resolving step.
 10. The imagecompensation method of claim 9 wherein the screening step comprisesremoving pixels containing clouds.
 11. The image compensation method ofclaim 1 wherein the spectral baseline determining step takes place usinga linear regression method.
 12. The image compensation method of claim 1wherein the spectral baseline determining step comprises determining theexcess reflectance at relatively short wavelengths relative to a flatspectral reflectance material.
 13. The image compensation method ofclaim 1 wherein the spectral baseline determining step comprisesdetermining the darkest signal from all of the image pixels for eachspectral band.
 14. The image compensation method of claim 1 wherein thespectral baseline determining step comprises determining the darkestsignal in any spectral band from all of the spectrally-diverse pixels.15. The image compensation method of claim 14 wherein the spectralbaseline determining step comprises subtracting a constant reflectancecontribution.
 16. The image compensation method of claim 15 wherein thespectral baseline determining step further comprises setting theconstant reflectance contribution to match the darkest signal at areference wavelength.
 17. The image compensation method of claim 1wherein the spectral baseline determining step is based on using aradiative-transfer code to compute the baseline based on the determinedaerosol and molecular optical properties
 18. The image compensationmethod of claim 1 wherein the statistical spectral deviation determiningstep comprises determining the standard deviation of thespectrally-diverse pixels.
 19. The image compensation method of claim 1wherein the normalizing step comprises normalizing in a spectral band inwhich the sun-surface-sensor path spectral transmittance factor is closeto unity.
 20. The image compensation method of claim 1 wherein thenormalizing step comprises resolving at least two spectral window bandsthat do not have a significant contribution from water absorption,correcting for Rayleigh scattering, using the spectral deviations ofthese window bands to retrieve an aerosol optical depth, and using theoptical depth to normalize.
 21. The image compensation method of claim 1wherein the normalizing step involves establishing the scale factor suchthat the maximum retrieved reflectance value for any wavelength andpixel of the spectrally-diverse pixels is unity.
 22. The imagecompensation method of claim 1 wherein the normalizing step comprisescomparing the spectrally-diverse pixels to a predetermined set ofspectra of different, known materials.
 23. The image compensation methodof claim 1 further comprising refining the spectrally-diverse pixels toremove spectrally-diverse pixels that contain undesirable spectralfeatures, the refining step taking place after the normalizing step andbefore the compensating step.
 24. The image compensation method of claim23 wherein the refining step comprises removing pixels with an abruptreflectance change around about 700 nm.
 25. The image compensationmethod of claim 23 wherein the refining step comprises removing pixelsthat have the greatest effect on the smoothness of the statisticalspectral deviation.
 26. The image compensation method of claim 23wherein the refining step comprises introducing a wavelength dependenceinto a normalization factor such that selected spectrally-diverse pixelsare made to agree with corresponding known library spectra as closely aspossible.
 27. A method of automatically determining a measure ofatmospheric aerosol optical properties using a multi- or hyper-spectral,multi-pixel image, comprising: resolving a plurality ofspectrally-diverse pixels from the image; determining a statisticalspectral deviation of the spectrally-diverse pixels; correcting thestatistical spectral deviation for non-aerosol transmittance losses; andderiving from the statistical spectral deviation one or morewavelength-dependent aerosol optical depths.
 28. The atmospheric opticalproperties measurement method of claim 27 wherein the resolving steptakes place with a spectral end member selection algorithm.
 29. Theatmospheric optical properties measurement method of claim 27 whereinthe resolving step takes place with a clustering algorithm.
 30. Theatmospheric optical properties measurement method of claim 27 whereinthe resolving step is accomplished manually.
 31. The atmospheric opticalproperties measurement method of claim 27 wherein at least ten endmembers are resolved.
 32. The atmospheric optical properties measurementmethod of claim 27 wherein the resolving step takes place using a subsetof spectral bands that span the spectrum of the image.
 33. Theatmospheric optical properties measurement method of claim 27 furthercomprising screening anomalous pixels out of the image pixels before theresolving step.
 34. The atmospheric optical properties measurementmethod of claim 33 wherein the screening step comprises removing pixelscontaining opaque clouds and cirrus clouds.
 35. The atmospheric opticalproperties measurement method of claim 27 wherein the statisticalspectral deviation determining step comprises determining the standarddeviation of the spectrally-diverse pixels.
 36. The atmospheric opticalproperties measurement method of claim 27 wherein the correcting stepinvolves using a radiative transfer code.
 37. The atmospheric opticalproperties measurement method of claim 27 wherein the deriving stepinvolves using a radiative transfer code.
 38. The atmospheric opticalproperties measurement method of claim 27 wherein the deriving stepcomprises performing a fit of the statistical spectral deviation to ananalytical representation of the aerosol transmittance.
 39. Theatmospheric optical properties measurement method of claim 27 whereinthe deriving step comprises performing a fit of the statistical spectraldeviation to a radiative transfer code.
 40. A method of automaticallydetermining a measure of atmospheric gaseous optical properties using amulti- or hyper-spectral, multi-pixel image, comprising: resolving aplurality of spectrally-diverse pixels from the image; determining astatistical spectral deviation of the spectrally-diverse pixels; andderiving from the statistical spectral deviation wavelength-dependentgaseous optical depths.
 41. The atmospheric gaseous optical propertiesdetermination method of claim 40 wherein the resolving step takes placewith a spectral end member selection algorithm.
 42. The atmosphericgaseous optical properties determination method of claim 40 wherein theresolving step takes place with a clustering algorithm.
 43. Theatmospheric gaseous optical properties determination method of claim 40wherein the resolving step is accomplished manually.
 44. The atmosphericgaseous optical properties determination method of claim 40 wherein atleast ten end members are resolved.
 45. The atmospheric gaseous opticalproperties determination method of claim 40 wherein the resolving steptakes place using a subset of spectral bands that span the spectrum ofthe image.
 46. The atmospheric gaseous optical properties determinationmethod of claim 40 wherein the statistical spectral deviationdetermining step comprises determining the standard deviation of thespectrally-diverse pixels.
 47. The atmospheric gaseous opticalproperties determination method of claim 40 wherein the deriving stepcomprises selecting reference spectral bands in molecular absorptionwindow regions, selecting molecular absorption bands, and deriving agaseous optical depth using the statistical spectral deviations at theselected bands.
 48. The atmospheric gaseous optical propertiesdetermination method of claim 47 wherein the deriving step comprisesselecting two reference bands nearby an absorption band, linearlycombining the reference bands to estimate the non-absorbing standarddeviation at the wavelength of the absorption band, forming a ratio ofthe absorption and estimated non-absorbing standard deviations, andderiving a gaseous optical depth for the absorption band using theratio.